Learning Small Decision Trees for Data of Low Rank-Width
AAAI 2024(2024)
摘要
We consider the NP-hard problem of finding a smallest decision tree
representing a classification instance in terms of a partially defined
Boolean function. Small decision trees are desirable to provide an
interpretable model for the given data. We show that the problem is
fixed-parameter tractable when parameterized by the rank-width of the
incidence graph of the given classification instance. Our algorithm
proceeds by dynamic programming using an NLC decomposition obtained
from a rank-width decomposition. The key to the algorithm is a
succinct representation of partial solutions. This allows us to limit
the space and time requirements for each dynamic programming step in
terms of the parameter.
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关键词
KRR: Computational Complexity of Reasoning
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